Predicting Worsted Spinning Performance with Artificial Neural Network Modeling

For a given fiber spun to a pre-determined yarn specification, the spinning performance of the yarn usually varies from mill to mill. For this reason, it is necessary to develop an empirical model that can encompass all known processing variables that exist in different spinning mills and then to generalize this information and be able to accurately predict yarn quality for an individual mill. This paper reports a method for the prediction of worsted spinning performance through the use of an artificial neural network (ANN) trained with backpropagation. The applicability of artificial neural networks for the prediction of spinning performance is first evaluated against a well established prediction and benchmarking tool (Sirolan Yarnspec). The ANN is subsequently trained with commercial mill data to assess the feasibility of the method as a mill specific performance prediction tool. The incorporation of mill specific data results in an improved fit to the commercial mill data set, suggesting that the proposed method has the ability to predict the spinning performance of a specific mill accurately.